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ObjectiveStroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis.DesignSystematic review and meta-analysis.Data sourcesA detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models.Eligibility criteriaAll studies that developed stroke prediction models in populations with diabetes were included.Data extraction and synthesisTwo reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic.ResultsOur search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68.ConclusionsOverall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models’ predictive ability and further external validation of the existing models in diverse population to improve generalisability.

In order to eliminate COVID-19, many countries provided vaccinations. However, success depends on peoples’ knowledge levels and rates of acceptance. But, previous research on this topic is currently lacking in Bangladesh. This cross-sectional study aimed at to investigate Bangladeshi peoples’ knowledge, acceptance, and perception of challenges regarding COVID-19 vaccines. Quantitative data were collected using an online survey (n = 1975) and face-to-face interviews (n = 2200) with a pre-tested structured questionnaire. In addition, seven open-ended interviews were conducted with health experts regarding challenges of vaccination. Binary logistic regression analyses were conducted to assess the association between explanatory and dependent variables. Effect size was estimated to understand the magnitude of relationship between two variables. Of 4175 respondents, 92.6% knew about COVID-19 vaccines, while only 37.4% believed vaccines to be effective in controlling COVID-19. Nearly 46% of respondents believed that COVID-19 vaccines have side-effects, and 16.4% of respondents believed that side-effects could be life-threatening. Only 60.5% of respondents indicated that they would receive the COVID-19 vaccine. Out of 1650 respondents (39.5%) who did not intend to receive the vaccine, 948 (57.4%) believed that they would be naturally protected. Regressions results indicated that men had higher rates of knowledge regarding the vaccine. In addition, rural respondents demonstrated lower knowledge regarding the vaccine. Furthermore, education had a significant association with knowledge of COVID-19 vaccines. Respondents with university education had more knowledge regarding the vaccine (Odds ratio, OR = 29.99; 95% confidence interval, CI 11.40–78.90, effect size 1.88; p = 0.01) and correct dosage (OR 27.34; 95% CI 15.25–49.00, effect size 1.83; p = 0.01). However, women (OR 1.16; 95% CI 0.96–1.40, effect size 0.08) and rural (OR 1.24; 95% CI 1.07–1.44, effect size 0.12; p = 0.01) respondents were more enthusiastic regarding receiving the COVID-19 vaccine. Higher educated respondents showed higher probability of receiving the vaccine. Those who believed in the effectiveness of the COVID-19 vaccine were 11.57 times more interested (OR 11.57; 95% CI 8.92–15.01, effect size 1.35; p = 0.01) in receiving the vaccine. Open-ended interviews identified several challenges toward successful COVID-19 vaccination. Mass awareness creation, uninterrupted supply, equitable distribution, and sectoral coordination were suggested to achieve at least 70% immunization across the country.

Research around probable solutions to immigrants accessing health care in Canada is not extensive, and the perspective of immigrant communities on priorities and potential solutions has not been captured effectively. The purpose of this article is to describe a research initiative that involved grassroots community members as producers of research priorities on primary care access issues. This study aimed to seek input from an immigrant community in Calgary, Canada. Members of the Bangladeshi community of Calgary were asked through a survey to rank 10 predefined primary care access topics as to what they felt constituted priorities for solution-oriented research (1, highest; 10, lowest). We used frequencies and percentages to describe the participant demographics. Ratings of preferred research themes were analyzed on the basis of relative weighted priority rank. We received 432 responses: 51.2% female; 58.9% aged 36 to 55 years; 90.5% had university-level education; 46.2% immigrated to Canada between 10 and 19 years ago; 82.5% employed full/part-time or self-employed. Lack of resources, lack of knowledge, health care cost, and workplace-related barriers were among the top-ranked topics identified as solution-oriented research priorities. Through partnerships and reciprocal learning, public input can increase insider perspectives to help develop interventions that align with the needs of community members.